Diclofenac pretreatment modulates exercise-induced inflammation in skeletal muscle of rats through the TLR4/NF-κB pathway
Bibliographic record
Abstract
Nonsteroidal anti-inflammatory drugs, such as diclofenac, are widely used to treat inflammation and pain in several conditions, including sports injuries. This study analyzes the influence of diclofenac on the toll-like receptor-nuclear factor kappa B (TLR-NF-κB) pathway in skeletal muscle of rats submitted to acute eccentric exercise. Twenty male Wistar rats were divided into 4 groups: control-saline, control-diclofenac, exercise-saline, and exercise-diclofenac. Diclofenac or saline were administered for 7 days prior to an acute eccentric exercise bout. The inflammatory status was evaluated through mRNA levels of cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), interleukin-6 (IL-6), IL-1β, and tumor necrosis factor alpha (TNF-α), and protein content of COX-2, IL-6, and TNF-α in vastus lateralis muscle. Data obtained showed that a single bout of eccentric exercise significantly increased COX-2 gene expression. Similarly, mRNA expression and protein content of other inflammation-related genes also increased after the acute exercise. However, these effects were attenuated in the exercise + diclofenac group. TLR4, myeloid differentiation primary response gene 88 (MyD88), and p65 were also upregulated after the acute eccentric bout and the effect was blunted by the anti-inflammatory drug. These findings suggest that pretreatment with diclofenac may represent an effective tool to ameliorate the pro-inflammatory status induced by acute exercise in rat skeletal muscle possibly through an attenuation of the TLR4-NF-κB signaling pathway.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".